Preamble

In this intro we take you through a focused approach for using R to generate data visualization elements from simulated data, this precise focus aims to enable you to use R more comfortably for your data visualization approaches. We will cover:

Installing R & RStudio

Install R

R is the programming language you’ll be using. Go to: https://cran.r-project.org Click on your operating system:

  • Windows → Click “Download R for Windows” → “base” → then click the .exe file to download.

  • macOS → Click “Download R for (Mac) OS X” → choose the .pkg file and install.

  • Linux → Follow instructions relevant to your Linux distribution.

Installing required packages

Open RStudio, you can run the following script to install the recommended set of packages. These are widely used for data analysis and report writing.

install.packages(c("tidyverse", "ggplot2", "dplyr", "rmarkdown", "knitr"))

Posit Cloud

Alternatively you can use all of the codes we provide to you here on a cloud version of R. For this:

  • Go to: https://posit.cloud

  • Click Sign Up (use your email, Google, GitHub, etc.)or Log In if you already have an account.

  • Once logged in, click “New Project”. This opens an RStudio environment in your browser — no installation needed.

Set up

The following code chunk includes the required packages in this EDA.

knitr::opts_chunk$set(echo = TRUE)

if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org")
if(!require(knitr)) install.packages("knitr", repos = "http://cran.us.r-project.org")
if(!require(DT)) install.packages("knitr", repos = "http://cran.us.r-project.org")
if(!require(plotly)) install.packages("plotly")
if(!require(ggplot2)) install.packages("ggplot2")
if(!require(dplyr)) install.packages("dplyr")
if(!require(ggplot2)) install.packages("ggplot2")
if(!require(gapminder)) install.packages("gapminder")
if(!require(gganimate)) install.packages("gganimate")
if(!require(ggalluvial)) install.packages("ggalluvial")

# Load
library(tidyverse)
library(knitr)
library(DT)
library(plotly)
library(ggplot2)
library(dplyr)
library(gapminder)
library(ggalluvial)

Data Visualisation

Creating data visualisation elements in R empowers you to explore, communicate, and understand complex patterns in large scale data with clarity and precision.

Through powerful packages like ggplot2, R enables the transformation of raw numbers into meaningful, customisable visuals and reproducible data visualistion elements. From simple line plots to interactive graphs and dashboards.

This visual storytelling enhances transparency, supports decision-making, and makes findings accessible to diverse audiences, including those without technical backgrounds.

Line plot

interactive line plot

Area graph

Scatterplots

Interactive plots

## `geom_smooth()` using formula = 'y ~ x'
## Setting the `off` event (i.e., 'plotly_doubleclick') to match the `on` event (i.e., 'plotly_hover'). You can change this default via the `highlight()` function.

Animated graphs

Sometimes even with interactive graphs, the picture is not complete. Adding movements to a complex graph can help make the relationship between the variables more transparent and add more meaning to the graphs. To achieve this, we use animations.

Bubble graphs

A template bubble graph using the airquality dataset in R.

River plots

River plots (also known as Sankey diagrams or alluvial plots) are useful for visualizing flow or transition between categories over time. Here’s how to create an alluvial/river plot using the gapminder dataset in R with the ggalluvial package. Click to maximise the plot.

This type of plots are highly relevant when you are investigating concepts such as disease progression where patients might move from one state to a more advance state of their disease throughout their life course.